Big data stream processing has many applications in social networks, image processing, internet of things (IoT), online control systems, information security, and machine learning. Many Services, such as internet services, cloud computing, portable devices and IoT, increasingly generate big data. Real-time processing of big data may be required for extracting valuable information and features from data, making decisions, and provisioning of services. A main infrastructure of big data processing is data centers. By increasing big data volume, the demand of data centers with more processing power may increase. However, dark silicon limitations may slow down CPU core scaling, leading to a decrease in processing power growth of data centers.
A few approaches entail utilizing hardware accelerators such as graphical processing units (GPUs) for accelerating stream data processing. However, existing solutions may be limited to specific applications. There is, therefore, a need for a reconfigurable method for utilizing hardware accelerators for stream data processing which may configure a hardware accelerator based on a desired operation.